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nnl-automl-model
VictorTishkevich/nnl-automl-model
nnl-automl-model is a image classification model from VictorTishkevich. Use it when you need a label for an image. It is set up for autogluon.
This model classifies photographs of food items as sweet (0) or savory (1). It fine-tunes a pretrained ResNet-50 with AutoGluon MultiModal.
Access
Public
Updated Sep 23, 2026
Repo size
268 MB
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Repo makeup
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.pkl94.5 MB · 52%
At a glance
- Task
- Image Classification
- Library
- autogluon
- Access
- Public
- Created
- Sep 21, 2026
- Updated
- Sep 23, 2026
- SHA
- e5657cf3
Model card
From the Hugging Face model README
Sweet vs Savory Classifier
This model classifies photographs of food items as sweet (0) or savory (1). It fine-tunes a pretrained ResNet-50 with AutoGluon MultiModal.
Inputs and outputs
Supply a pandas DataFrame with an image column containing local image-file paths. The predictor reads the pixels; labels and provenance fields are not inputs. predict() returns 0 or 1, and predict_proba() returns class probabilities.
| Label | Meaning |
|---|---|
0 | Sweet |
1 | Savory |
Data and training
Source Dataset: kwongnon/2026-24679-image-dataset.
| Training setting | Recorded value |
|---|---|
| Framework | AutoGluon MultiModal 1.6.1 |
| Image backbone | timm resnet50, pretrained, fine-tuned for two classes |
| Preset and branch | medium_quality; model.names = ["timm_image"] |
| Selection metric | Validation accuracy |
| Fit budget and seed | 300 seconds; 24679 |
| Recorded environment | Linux; Python 3.13.15; PyTorch 2.11.0+cpu |
The notebook reports 327.53 seconds of training for the final ResNet-50 model with a final validation accuracy of 0.7143.
Evaluation
| Test metric | Value |
|---|---|
| Test Accuracy | 0.3750 |
| Weighted F1 | 0.3849 |
- Task
- Image Classification
- Library
- autogluon
- Created
- Sep 21, 2026
- Updated
- Sep 23, 2026